;;  _________________________________________________________________________________________________________________
;;
;;  ----------------------   Diminished-F'NF   ----------------------------------------------------   Diminished-F'NF
;;  Diminished-F'NF   ------------------------------------------------   Diminished-F'NF   --------------------------
;;  _________________________________________________________________________________________________________________



breed     [flibs flib]     ;; FLiBs (finite living blobs) are the agents of the model: they are structured as
                           ;; one state finite automata. A binary input signal coming from the previous state of
                           ;; the environment (1 if the bar was crowded, 0 if not) causes it to generates binary
                           ;; signals as a decision to go or not to go. Four strategies are possible.


flibs-own [chromosome      ;; every flib owns a chromosome, that is a string codifying one of the four schema
           state           ;; the current inner state of the flib: it is always 0
           choice          ;; choice/prevision expressed by the flib regarding to go or not to go to the bar
           fitness         ;; a measure of flibs' prevision ability to forestall if the bar will be crowded or not
           ]


globals  [tot_attend        ;; total of attendances during one season to "El Farol" bar (i.e. 100 cycles tournement)
          sigma_attend      ;; an accumulator for the previous variable
          crowded           ;; a switch recording the state of the bar: 1 if crowded, 0 if not
          lorenz-points     ;; list of the Lorenz curve ordinates
          gini-index-reserve;; counter functional to the calculation of the Gini index
          sigma-gini        ;; an accumulator for the previous variable
          best              ;; the best flibs fitness value
          worst             ;; the worst flibs fitness value
          ]


;;  ----------   SETUP PROCEDURES   ----------------------------------------------------------------------------------
;;  ------------------------------------------------------------------------------------------------------------------

to setup           ;; initializing the model
  clear-all
  ;; create the bar area (yellow) and an elsewhere (blue)
  ask patches  [set pcolor blue - 3]
  ask patches with [abs pxcor < 10 and abs pycor < 7] [set pcolor yellow]
  ask patches with [pxcor = 9 and pycor = 7] [set plabel ["El Farol Bar"]]
  ask patches with [pxcor = 21 and pycor = -21] [set plabel ["Elsewhere"]]

  ;; create the flibs and give them a random chromosome
  ask n-of num-flibs patches [sprout-flibs 1 [
    set shape "flib"
    set color white
    set size 2

    set chromosome one-of chrom-couple
    ]
  ]
  reset-ticks
end 



;;  ----------   RUNTIME PROCEDURES   --------------------------------------------------------------------------------
;;  ------------------------------------------------------------------------------------------------------------------

to go
  set tot_attend 0
  ask flibs [set fitness 0 set state 0]

  repeat 100 [el-farol] ;; a 100 cycles tournament could be seen as a season to "El Farol" bar (i.e. 100 evenings)

  if sum [fitness] of flibs = 0 [  ;; no fitness no progress
    show "fitness null for every flibs"
    stop
  ]
  analyse   ;; some relevant "El Farol" seasonal results are picked and processed
  ask flibs [move] ;; the world displays a snapshot of the bar after the last evening of the season

  if best != worst[ ;; after every season, one cloning event can occur: the process is partly selective
    ask one-of flibs with [fitness = worst]
      [set chromosome one-of chrom-couple
    ]
  ]
  tick
end 


;; ONE SEASON TO EL FAROL BAR
;; -------------------------------------------------------------------------------------------------------------------

to el-farol
  let attendance 0    ;; the variable records the fraction of agents attending the bar during one evening
  flibs-behaviour
  ;; bar attendance is the sum of every flibs' choices
  set attendance sum [choice] of flibs / num-flibs
  ;; comparing the attendance and the threshold value, the (over)crowded state of the bar is determined
  if attendance >= threshold [set crowded 1    ;; if the bar is crowded, reward the flibs that are elsewhere
    ask flibs with [choice = 0] [set fitness fitness + EW_reward] ]
  if attendance < threshold [set crowded 0  ;; if the bar is not crowded, reward the flibs that are attending
    ask flibs with [choice = 1] [set fitness fitness + 1] ]
  set tot_attend tot_attend + attendance ;; the results of every evening of a season is added up
end 

to flibs-behaviour
  ask flibs [
    ;; each flib processes its choice (to go or not to go)
    set choice read-from-string item (2 * crowded) chromosome
  ]
end 

to analyse
  set best max [fitness] of flibs
  set worst min [fitness] of flibs
  ask flibs [set color scale-color red fitness 100 0]
  set sigma_attend sigma_attend + tot_attend / 100
  update-lorenz-and-gini
end 

to update-lorenz-and-gini  ;; borrowed from Wilensky's model "Wealth Distribution"
  let sorted-comfort sort [fitness] of flibs
  let total-comfort sum sorted-comfort
  let comfort-sum-so-far 0
  let index 0
  set gini-index-reserve 0
  set lorenz-points []
  repeat num-flibs [
    set comfort-sum-so-far (comfort-sum-so-far + item index sorted-comfort)
    set lorenz-points lput ((comfort-sum-so-far / total-comfort) * 100) lorenz-points
    set index (index + 1)
    set gini-index-reserve
      gini-index-reserve +
      (index / num-flibs) -
      (comfort-sum-so-far / total-comfort)
    ]
  set sigma-gini sigma-gini + ((gini-index-reserve / num-flibs) * 2)
end 

to move
  ifelse choice = 0
      [move-to one-of patches with [pcolor = blue - 3]]
    [move-to one-of patches with [pcolor = yellow]]
end 



; Copyright 2023 Cosimo Leuci.
; See Info tab for full copyright and license.